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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title> Foundational Study for Transportation Network Cyber Threat Detection Using AI/ML
</title>
      <link>https://rip.trb.org/View/2719314</link>
      <description><![CDATA[Primarily, this effort will build a foundational software prototype to identify, source, and package data for use in machine learning (ML)-based anomaly detection models. Secondly, Georgia Tech (GT) will work in close partnership with Georgia Department of Transportation (GDOT) to assess one (1) out of the eight (8) representative cyber threats and platforms, and characterize detection performance using standardized performance measures (e.g., classification accuracy, precision and recall).
]]></description>
      <pubDate>Thu, 25 Jun 2026 11:32:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2719314</guid>
    </item>
    <item>
      <title>Prototype Development and Pilot Deployment of Ground-Based Intelligent Infrastructure for Resilient Positioning, Navigation, and Timing</title>
      <link>https://rip.trb.org/View/2696990</link>
      <description><![CDATA[Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), form the backbone of modern positioning, navigation, and timing (PNT) services. However, these space-based systems are inherently vulnerable to cyberattacks such as jamming, spoofing, as well as unintentional interference, including signal blockage, particularly in dense urban areas, indoor environments, and adversarial environments. The growing dependence on GNSS, driven by the rapid adoption of autonomous and connected systems, has exposed a single point of failure in the global PNT infrastructure. GPS signals are extremely weak at the Earth’s surface, enabling low-cost jammers or spoofers to easily disrupt receivers. In response to the 2020 Executive Order on strengthening national resilience through responsible use of PNT services signed by President Donald J. Trump, US DOT, the Department of War (DoW), and the Department of Homeland Security (DHS) have jointly emphasized the need for complementary and backup PNT capabilities that are interoperable and independently capable of sustaining precision timing and navigation for critical infrastructure during GNSS outages or cyberattacks. The research goal is to develop and demonstrate a prototype ground-based, GPS-compatible, cyber-secure PNT architecture that can generate, synchronize, and broadcast authenticatable GPS-like signals from a network of ground-based nodes, allowing existing GPS receivers to obtain valid PNT solutions without hardware modification. This goal will be achieved through the following specific research objectives: (1) Design and generate authenticable GPS-compatible terrestrial signals that replicate the L1 C/A (coarse/acquisition) waveform while embedding virtual ephemeris and adjusted clock-offset parameters to enable accurate and PNT computation from ground transmitters. (2) Develop intelligent terrestrial nodes (at least four nodes) equipped with chip-scale atomic clocks, edge computer, and transmitters to establish a distributed ground-based PNT architecture. (3) Synchronize terrestrial nodes with a master clock using precision timing distribution techniques to maintain consistent and reliable time alignment across the network. Real-Time Kinematic (RTK) positioning and differential methods will also be explored using the GEODNET hub within the UA network. (4) Demonstrate that an off-the-shelf GPS receiver can deliver a valid PNT solution using terrestrial signals through software-only modifications, thereby validating the practicality, backward compatibility, and deployment readiness of the proposed system.
]]></description>
      <pubDate>Wed, 29 Apr 2026 16:45:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696990</guid>
    </item>
    <item>
      <title>Safe and Reliable Autonomous Vehicle Navigation through Cyber Resilience</title>
      <link>https://rip.trb.org/View/2531083</link>
      <description><![CDATA[The reliable operation of Autonomous Vehicles (AVs) hinges on robust and reliable Positioning, Navigation, and Timing (PNT) services, predominantly provided by Global Navigation Satellite Systems (GNSS). The U.S.-owned Global Positioning System (GPS) consists of Ground Control Stations (GCS), Space Vehicles (SV), and user segment receivers, all of which could be susceptible to natural interferences and cyber threats. GCS, vulnerable to physical and cyberattacks, can transmit compromised correction data to satellites, posing significant risks to navigation integrity. GNSS signals are inherently weak and susceptible to unintentional interference, such as signal blocking, urban canyon multipath, and atmospheric effects, as well as deliberate threats like jamming and spoofing, which significantly amplify uncertainties in PNT services. Although alternative PNT solutions, including Low Earth Orbit (LEO) satellites, Wi-Fi, and cellular-based technologies, show promise, they remain limited in coverage, underdeveloped, and/or vulnerable to intentional interference. High-definition (HD) map-based navigation systems are also at risk of exploitation by hackers. Multi-sensor fusion systems, integrating GNSS with inertial measurement units (IMU) and perception sensors (PS), such as cameras, LiDAR, and RADAR, offer potential solutions by complementing individual sensor outputs in contested environments. However, IMUs suffer from error accumulation, and PS performance is compromised by limited line-of-sight or adverse weather conditions (e.g., snow and heavy rain), which degrade positioning accuracy. To overcome these challenges, the overarching goal of this project is to enhance the security of GNSS-based navigation systems through four key objectives: (1) identifying and analyzing vulnerabilities in GNSS ground control and user segments to develop intelligent cyber-attack models, (2) designing and implementing sensor fusion algorithms that leverage loosely coupled GNSS, IMU, and perception sensor data for the detection of GNSS cyber-attacks, (3) developing advanced mitigation strategies to counter spoofing attacks and restore authentic GNSS signal lock, and (4) deploying these detection and mitigation algorithms in secured execution environments (TEEs) to safeguard operational integrity against software-based threats. By addressing GNSS vulnerabilities, the research will significantly enhance the safety and reliability of GNSS-based navigation for autonomous vehicles, foster public and industry reliability in these technologies, and support broader advancements in transportation cybersecurity.]]></description>
      <pubDate>Mon, 31 Mar 2025 17:16:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2531083</guid>
    </item>
    <item>
      <title> Deploying Smart Watch Technology to Measure and Mitigate Heat Stress Among Maritime Transportation Workers </title>
      <link>https://rip.trb.org/View/2406735</link>
      <description><![CDATA[Workers in the maritime transportation industry are often exposed to high-heat and high-humidity conditions, exacerbating their risk of developing heat-related illnesses. These illnesses range in severity from muscle cramps and spasms; to heat exhaustion; to heat stroke, a life-threatening emergency that requires immediate medical attention. The Occupational Safety and Health Administration has identified a variety of maritime transportation industries as heat-related “high risk”. If early warning indicators of heat stress can be identified, then the possibility of a worker developing a heat-related illness can be mitigated. Smartwatches have the potential to function as a means for detecting when a heat-related illness is imminent and/or progressing. This study was undertaken to research the following questions: 1) What are the key indicators that can be used to quantify heat stress? 2) Can a smartwatch be used to measure heat stress among maritime transportation workers? 3) Can heat stress predictive models be used to prevent the development of heat-related illnesses by incorporating a complete closed feedback loop? Collectively, this exploration aims to provide a comprehensive understanding of how heat stress can be better monitored and managed in occupational settings, laying the groundwork for developing practical applications that achieve timely and cost-effective heat stress detection and mitigation.]]></description>
      <pubDate>Tue, 23 Jul 2024 16:18:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2406735</guid>
    </item>
    <item>
      <title>BikePed Portal: Pedestrian Volume Estimation Based on Push Button Actuations from Signals Data</title>
      <link>https://rip.trb.org/View/2361978</link>
      <description><![CDATA[This project translates research from Oregon DOT's "Active transportation counts from existing on-street signal and detection infrastructure" (SPR 857), into a practical application on BikePed Portal. ]]></description>
      <pubDate>Tue, 02 Apr 2024 13:42:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2361978</guid>
    </item>
    <item>
      <title>Multimodal In-Vehicle Sensor Fusion for Cyber-Secured Autonomous Navigation</title>
      <link>https://rip.trb.org/View/2334647</link>
      <description><![CDATA[Successful navigation of autonomous vehicles relies on positioning, navigation, and timing (PNT) services. Global Navigation Satellite Systems (GNSS), such as Global Positioning System (GPS) (USA), BeiDou/BDS (China), Galileo (Europe), GLONASS (Russia), IRNSS/NavIC (India), and QZSS (Japan), provide PNT services. However, GNSS signals are vulnerable to unintentional interference (e.g., jamming caused by walls and ceilings in garages and tunnels, and multipath issues due to high-rise buildings in urban areas) and deliberate cyber threats (e.g., jamming and spoofing of GNSS signals).  Prior research shows that the use of multi-sensor fusion systems—i.e., GNSS with inertial measurement unit (IMU) and perception sensors (PS) (e.g., camera, LiDAR, RADAR)— could complement each other and correct the individual sensor output and determine reliable navigation solution under deliberate threats and GNSS-denied environments (e.g., GNSS outage and/or INS error accumulation issue and/or PS view obstruction).  However, IMU and PS can only provide relative positioning and rely on GNSS for absolute positioning. Even advanced INS (GNSS+IMU) provide cm level accuracy; however, during GNSS outage, it could accumulate position error up to 3.80 meters in just 1 minute due to error accumulation of inertial sensors. Thus, the major research gap is to comprehensively identify and understand GNSS vulnerabilities in autonomous vehicles, investigate realistic attack modeling, detection, and develop cyber-resilient navigation solutions for GNSS-based navigation.
The overarching research goal of this project is to understand the vulnerabilities of GNSS-based navigation, develop intelligent slow-drifting cyber-attacks, develop corresponding attack detection models, and devise cyber-resilient navigation solutions to enhance the GNSS-based navigation system. The research goal will be achieved through the following research objectives: (1) investigate and develop intelligent slow-drifting GNSS spoofing attacks by manipulating GNSS signal’s navigation data; (2) investigate and develop GNSS cyber-attack detection algorithms for slow-drifting GNSS spoofing attacks; and (3) develop a secure in-vehicle sensor fusion-based navigation module using deep fusion algorithms during a GNSS-denied environment. The outcomes of this project will be to implement and validate intelligent slow-drifting GNSS spoofing attack models using a GNSS receiver in both laboratory and real-world environments, evaluate GNSS cyber-attack detection algorithms against intelligent slow-drifting GNSS spoofing attacks through field testing, and demonstrate proof-of-concept of an in-vehicle sensor fusion-based cyber-resilient navigation solution in a controlled, real-world environment.
]]></description>
      <pubDate>Fri, 09 Feb 2024 19:41:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2334647</guid>
    </item>
    <item>
      <title>System Design for Highly Accurate and Efficient Target Detection in Triaxial Testing</title>
      <link>https://rip.trb.org/View/2289621</link>
      <description><![CDATA[For photogrammetry-based volume measurement, existing coded target (CT) recognition and identification algorithms have limitations in perspective deformation, freely rotated CTs, and unfavorable light conditions. This study will develop an innovative system design for highly accurate and efficient target detection in triaxial testing. The proposed method will remain all the merits in existing methods and have several improvements, including blob analysis, automatic outlier identification, and an increased number of points on the membrane for more representative 3-D results. The developed photogrammetry-based volume measurement method with the target detection technology will be applied in the widely used triaxial tests to evaluate stress-strain behavior of geomaterials. The method will improve the testing accuracy and efficiency. The low-cost testing system has the potential to be widely adopted by government agencies, contractors, and research institutes.]]></description>
      <pubDate>Tue, 14 Nov 2023 20:26:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2289621</guid>
    </item>
    <item>
      <title>Aerial Infrared Scanning of Bridge Decks for Detecting and Mapping Delamination</title>
      <link>https://rip.trb.org/View/2190082</link>
      <description><![CDATA[This research is to evaluate the condition of Alaska DOT bridge decks located along the Parks Highway. The deck condition evaluations will be carried out using aerial infrared thermography (aerial IF) and corresponding visual imaging data collected from a fixed wing aircraft. Work will include the following deliverables: a final report including a description of the equipment, the data collection and analysis procedures, results of analysis, detailed comparison of aerial IR versus ground-truth data, ROI analysis, and recommendations for future implementation based on the results.]]></description>
      <pubDate>Fri, 02 Jun 2023 19:19:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2190082</guid>
    </item>
    <item>
      <title>Improving Subsurface Non-metallic Utility Locating Using Self-aligning Robotic Ground-penetrating Radar</title>
      <link>https://rip.trb.org/View/2093163</link>
      <description><![CDATA[The project will develop a pre-commercial prototype robotic locating system. This system will use GPS and adaptive ground probing radar sensors to improve the quality of image and location data.]]></description>
      <pubDate>Tue, 03 Jan 2023 13:53:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2093163</guid>
    </item>
    <item>
      <title>NCHRP Implementation Support Program. Implementation of Asphalt Pavement Raveling Detection Algorithm</title>
      <link>https://rip.trb.org/View/2054772</link>
      <description><![CDATA[State departments of transportation (DOTs) use different pavement surfaces, such as open-graded friction courses (OGFC), seal coats, and chip seals. Raveling (loss of aggregates) is a predominant pavement distress that impacts the safety and functionality of the pavement surface. Some state DOTs have classified raveling into different severity levels to determine the appropriate pavement preservation actions. However, current practices for the visual inspection of the severity levels of raveling are time-consuming, labor-intensive, and, most importantly, subjective.

NCHRP IDEA 20-30/IDEA 163, "Development of an Asphalt Pavement Raveling Detection Algorithm using Emerging 3-D Laser Technology and Macrotexture Analysis," successfully developed automatic raveling detection and classification algorithms using three-dimensional (3D) pavement surface condition data, macro-texture analysis, and machine learning (ML) modeling. Different ML models were critically evaluated, and automatic raveling detection and classification algorithms were developed. The algorithms use the 3D pavement surface data already collected by state DOTs for the evaluation of cracking and rutting in pavements, so additional data collection effort is not needed. The output from the automatic raveling detection and classification algorithm is the severity level (severe/3, medium/2, and low/1) based on the 3D pavement images collected at different image sizes (e.g., 5-meter or 8-meter intervals) as specified by the different 3D sensing systems used. 

Based on the process described in NCHRP IDEA 20-30/IDEA 163, Florida DOT developed its own algorithm for classifying pavement raveling. The Florida DOT algorithm, which was written in Python, uses a random forest classifier, and the algorithm development included a training module where human raters looked at images and classified them. This algorithm can also read images, process them, and classify raveling by severity. 

OBJECTIVE: The objective of this research is to develop guidelines for implementing the automatic raveling detection and classification algorithms developed in NCHRP IDEA 20-30/IDEA 163 in available programming languages or commercial software, such as Python or MATLAB. State DOTs should be able to use the products to calibrate and refine the algorithms for future use, which may include the use of different severity classifications and surface types, as well as incorporate the algorithms in their pavement management system or use the algorithms to make maintenance and rehabilitation decisions.]]></description>
      <pubDate>Tue, 01 Nov 2022 06:17:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2054772</guid>
    </item>
    <item>
      <title>Detection and Monitoring of Material Aging and Structural Deterioration using Electromagnetic and Mechanical Sensors with Virtual Reality and Machine Learning Modeling (3.19)</title>
      <link>https://rip.trb.org/View/1994582</link>
      <description><![CDATA[The problem we are trying to solve is the detection and monitoring of aging civil infrastructure components and systems in New England by using visual information and subsurface images in a virtual reality (VR) environment for data visualization and machine learning (ML) for data interpretation. Material aging and structural deterioration of selected candidate structures (e.g., highway bridges) will be frequently (from twice a day to once a week) inspected to develop large amount of sensor data for condition assessment using machine learning. The problem is important because, with frequent inspection of civil infrastructure systems, processing and visualization of large amount of multiple-format sensor data have become a challenging task at the system’s level for bridge engineers. Registration of 2D photographs and sensor images in a 3D environment aided with VR equipment (e.g., headsets, handles, controllers) can help bridge engineers to better register inspection and monitoring data in multiple formats (e.g., photographs, images, texts, sketches) to actual structures. Frequent inspection of structures can produce large amount of data required by machine learning, as well as capturing the weekly, monthly, and seasonal changes of background/baseline information. In this base funded project, we propose to 1) collect electromagnetic (EM) (e.g., optical, radar, and laser sensors) and mechanical (e.g., impact-echo, ultrasonic testing, pulse tomography sensors) sensor data on a frequent basis (from twice a day to
once a week) to develop large amount of data for machine learning interpretation, 2) study the effect of material aging on structural deterioration of highway bridges, and 3) develop a VR platform for rendering sensor data (including bridge rating and inspection reports) of inspected bridges in a 3D environment for the convenient interpretation of sensor data.]]></description>
      <pubDate>Fri, 15 Jul 2022 15:24:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1994582</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 54-14. Artificial Intelligence Applications for Automated Pavement Condition Evaluation</title>
      <link>https://rip.trb.org/View/1953242</link>
      <description><![CDATA[3D laser-based pavement imaging systems have been widely adopted by state departments of transportation (DOTs) in the last decade for automated pavement condition survey (APCS) assessment; 2D imaging technologies and smartphones are also used to perform pavement condition evaluations, especially for local transportation agencies. Collected pavement images are then used to semi- or fully automatically extract pavement distresses through various methods. Among these methods, models based on artificial intelligence (AI) with machine learning and deep learning (ML/DL) have gained much attention for pavement distress identification in the last several years. However, most AI models either are not yet fully integrated with how state DOTs use the pavement distress data or have not been sufficiently developed to employ quality 3D pavement image data.

The collected distresses, such as cracking, faulting, flushing, and raveling, are key indicators of triggering pavement maintenance and rehabilitation activities. Without a clear understanding of state DOTs’ ultimate use of this distress data, AI model development efforts for distress detection and/or classification, which include AI model formulation, distress annotation, training, and performance evaluation, could be misguided and fail to reach their full potential. For example, the AI-based models for automated crack detection using the classification of image blocks with cracking distress may not be able to output accurate cracking length and width information. Therefore, the outcome produced by the model may not meet the state DOT’s need for project-level applications, such as planning crack sealing projects. Alternatively, the performance of supervised learning AI models for automated pavement distress extraction relies heavily on several factors, including the quality of the pavement image data used, data size and diversity, the annotation quality (labeled ground truth distresses), the model formulation, model training, and so forth. However, the performance evaluation method used for many developed models is not always clear, especially for the diversity of the data used for that evaluation and its established ground truth. This ambiguity makes comparing the performance of different models challenging and unreliable.

The objective of this synthesis was to document current state DOT practices for both automated pavement distress identification and AI (ML/DL) technologies for pavement condition evaluation. Information for this study was gathered through a literature review, a survey of state DOTs, and follow-up interviews with selected DOTs. However, the relatively recent development and implementation of 3D technology and the use of AI for APCS analysis resulted in difficulties identifying any state DOT that can to provide details and specifics for case example development. Therefore, in lieu of case examples, the report provides a general summary of efforts made for AI model development and training.

Linda M. Pierce, Sarah E. Lopez, Jose R. Medina, and Vivek Jha of NCE collected and synthesized the information and wrote the report. The members of the topic panel are acknowledged on page iv. This synthesis is an immediately useful document that records the practices that were acceptable within the limitations of the knowledge available at the time of its preparation. As progress in research and practice continues, new knowledge will be added to that now at hand.]]></description>
      <pubDate>Tue, 17 May 2022 10:21:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/1953242</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 54-13. Truck Emergency Escape Ramp Design and Operation</title>
      <link>https://rip.trb.org/View/1953231</link>
      <description><![CDATA[Transportation agencies build and operate escape ramps to allow heavy vehicles that cannot maintain safe speeds on steep downgrades to safely exit the highway before losing control and crashing. Escape ramps involve important design choices specific to roadway, terrain, and vehicle characteristics. Some escape ramps use upward-sloping beds of loose aggregate to slow the vehicles, while others employ passive or active mechanical devices such as cables to retard their motion. The use of escape ramps also requires operational choices. Some agencies use intelligent transportation technologies to detect trucks that are exceeding or may soon exceed speeds appropriate for the vehicle and the location, and to warn drivers of the need and means to exit the roadway.

 

Because design and operations practices vary significantly among locations and agencies, no common design standards or recommended practices exist. Brake failures on heavy vehicles descending steep grades often result in serious crashes and fatalities.

 

The objective of this synthesis is to document practices used by state departments of transportation (DOTs) to design and operate facilities to detect, guide, and capture out-of-control vehicles travelling on steep downgrades.]]></description>
      <pubDate>Mon, 16 May 2022 17:37:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/1953231</guid>
    </item>
    <item>
      <title>Framework of Internal Damage Identification in Inhomogeneous Medium Interweaving Wave Scattering Model and Deep Learning </title>
      <link>https://rip.trb.org/View/1948952</link>
      <description><![CDATA[Various environmental conditions and loading forces may cause infrastructure material,
concrete, and hot mix asphalt (HMA) to deteriorate. In particular, internal vertical cracks and internal reflective
cracks (e.g., subsurface cracks) perpendicular to concrete surfaces are the most common,
challenging, and critical types of infrastructure damage. Consequently, these extensive damages
result in material property degradation, reinforcement corrosion, and even structural failure. Thus,
effective detection of the cracks must be executed in a timely manner for better service life
prediction and to monitor structural conditions at an early stage. There are recent advances in the
study of surface-opening vertical crack detection (e.g., nonlinear diffuse ultrasonic waves, guided
waves, and transmission energy). Despite these efforts, these studies for surface opening crack
not internal damage, may present certain limitations and challenges for more in-depth
understanding and monitoring of "internal" cracks. In particular, these internal reflective cracks
commonly occur in many other infrastructures such as airport runway, pavement, and pipe, under
the overlay caused by stress concentration at the bottom of the overlay.
PI recently studied an analytical model to identify the internal reflective crack with various
numerical integration methods to improve the analytical solution validated through finite element
(FE) simulations and experimental study [8]. The advantage of this approach is that it provides an
accurate depth-to-crack distance by using the relation between scattering energy, so-called wave
response variation (WRV), and crack geometry. However, huge challenges in this effort of the
analytical modeling for identifying are to reduce the gap between the nonlinear analytical and
numerical WRV model and experimental WRV result; to identify the material inhomogeneity effect
in the wave scattering model (WSM); to define the physics-based interpolations with machine learning (ML) technique. The followings are
primary research gaps that need to be addressed in this project.
Consequently, the project's overall goal is to advance understanding of a WSM of an internal vertical reflective crack in inhomogeneous material (IHM) leveraging deep
learning. The testing data and its analysis of WRV by the crack and toward the establishment of
a unique analytical model will be then integrated into IWSM with the physics-based ML interpolation for complex features and environments, potentially for large
applications (e.g., buried concrete pipe in soil, one side accessible slab, reflective cracks from the
concrete pavement joint). The project will also carry out the Trans-SET missions by performing
research, technology transfer, education, workforce development, and outreach activities to solve
transportation challenges in Region 6.]]></description>
      <pubDate>Mon, 09 May 2022 06:23:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/1948952</guid>
    </item>
    <item>
      <title>Open Framework Standards for Combined Aircraft Sensor Network for the State of Ohio to Detect and Track Lower Altitude Aircraft
</title>
      <link>https://rip.trb.org/View/1875931</link>
      <description><![CDATA[Since 2013, the Ohio Unmanned Aircraft Systems (UAS) Center has managed and performed all unmanned aircraft operations for the Ohio Department of Transportation (ODOT). The UAS Center also serves as a shared resource to local and state agencies for flight operations and UAS program development. In addition, the UAS Center manages innovative initiatives focused on enabling the lower altitude airspace and integration of unmanned and autonomous aircraft technologies into the National Airspace System (NAS).   
Several initiatives have been undertaken and more are being proposed to support activities involving the safe movement of people and products in the NAS. The aircraft performing these UAS activities range from small unmanned or remotely piloted aircraft to larger piloted, optionally piloted, remotely piloted to fully unmanned aircraft. Currently there is a variety of equipment, data configurations and such being utilized throughout the nation in the field of UAS technology. This field is continually evolving with the development and deployment of new technologies. As a result, it can be difficult for local, state and federal agencies to share critical information to maintain the safety of the lower altitude airspace. As the lower altitude airspace is utilized by more businesses and agencies and the deployment of UAS increases, the need to detect and manage aircraft in the lower altitude airspace is amplified. To streamline the sharing of data and ensure a safe environment in the lower altitude airspace research is needed.
The goal of this research is to create a comprehensive framework for all low altitude airborne sensor data (including local, state and federal entities) to be combined into a centralized clearinghouse. This data would be and managed through a statewide low altitude air traffic monitoring center and accessible to all parties thereby enhancing the safety and security for all low altitude aircraft in Ohio. 
This research will help determine a minimum standard for radars and data to allow Ohio to a develop a comprehensive system for lower altitude aircraft. It will streamline efficiencies in this field statewide and continue to keep Ohio at the forefront of this industry.               ]]></description>
      <pubDate>Wed, 01 Sep 2021 09:58:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/1875931</guid>
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